A Statistical Learning Framework for Phylogenetic Inference: Information, Uncertainty, and Geometry

系统发育学是从分子序列数据中研究个体或生物群体之间的进化关系，是生物学研究的一个主要主题。在过去的几十年里，系统发育推断的可用数据量的爆炸式增长为我们进一步了解各种生物过程提供了巨大的机会。与此同时，它们也将系统发育引入了一个新的学习机制，在这个机制中，传统理论不再能够指导系统发育算法的发展和解释。本研究的主要目的是通过统计学习和信息论的观点来提高我们对系统发育中心概念的理解，并为在这种新的学习环境下进行系统发育分析提供必要的工具。通过提供一个框架来设计、分析和改进系统发育估计器，该研究将大大扩展可获得可靠分析的问题集。最值得注意的是，当物种数量增加时，我们的研究特别适合于小进化单位的生物学，包括病毒和抗体制造b细胞的研究。这项研究的教育部分包括指导本科生和研究生进行系统发育的独立研究，并将为系统发育推断提供各种演示、教程和统计包。该研究为系统发育信息和不确定性的明确量化奠定了基础，重点关注序列数据不断生成和分析的环境。这种方法可以使用局部系统发育方法作为分析基于似然的方法的一种手段，并有助于研究在保留基本信息的同时稳定不确定性的系统方法。在非渐近环境下构建和分析系统发生方法的两种分析工具将被开发：一类新的进化相关随机变量的集中不等式和系统发生树空间上系统发生可能性的泰勒式局域到全局扩展。这些新衍生的工具将用于研究几个重要的推理问题，包括病毒系统发育和性状进化中的物种树/超树重建和参数估计。这些研究活动将对系统发育信息和不确定性对系统发育估计稳定性的影响提供重要的见解。该奖项由DMS统计学项目和生命科学风险基金共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Phylogenetics, the study of the evolutionary relationships among individuals or groups of organisms from molecular sequence data, is a dominant theme in biological research. In the last few decades, the explosion in the amount of available data for phylogenetic inference has offered great opportunities to further our understanding of various biological processes. At the same time, they also move phylogenetics to a new learning regime, where traditional theories can no longer guide developments and interpretations of phylogenetic algorithms. The main goals of this research are to improve our understanding of the central concepts of phylogenetics through the viewpoints of statistical learning and information theory and to provide essential tools for phylogenetic analyses in this new learning setting. By providing a framework to design, analyze and improve phylogenetic estimators, the research will greatly extend the set of problems for which reliable analyses can be obtained. Most notably, our research in the setting when the number of species increases is especially amenable to the biology of small evolving units, including studies of viruses and antibody-making B-cells. The education component of this study involves mentoring undergraduate and graduate students with independent research in phylogenetics and will produce various demonstrations, tutorials and statistical packages for phylogenetic inference.The proposed research lays out the foundation for explicit quantification of phylogenetic information and uncertainty, with a focus on the setting where sequence data are continually being generated and analyzed. This approach enables the use of local phylogenetic methods as a means to analyze likelihood-based methods and helps investigate systematic ways to stabilize uncertainty while retaining essential information. Two analytical tools to construct and analyze phylogenetic methods in non-asymptotic settings will be developed: a new class of concentration inequalities for evolutionary-related random variables and a Taylor-like local-to-global expansion of phylogenetic likelihood on the space of phylogenetic trees. These newly derived tools will be used to study several important inference problems, including species tree/supertree reconstruction and parameter estimation in viral phylogenetics and trait evolution. The research activities will provide important insights into the influences of phylogenetic information and uncertainty on the stability of a phylogenetic estimate.This award is co-funded with the Statistics program and the Life Science Venture Fund in DMS.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.